|
11th ACM/SPEC International Conference on Performance Engineering Edmonton, Alberta, Canada April 20-24, 2020
https://icpe2020.spec.org
Important dates:
Research Papers and Artifacts
Abstract submission: Oct 4, 2019
Paper submission: Oct 11, 2019
Industrial/Experience Papers
Abstract submission: Oct 4, 2019
Paper submission: Oct 11, 2019
Workshop Proposals:
Proposal submission: Oct 11, 2019
Poster and Demo Papers, Tutorials, Work-in-Progress:
Proposal submission: Jan 20, 2020
Details:
https://icpe2020.spec.org/call-for-contributions/
(https://icpe2020.spec.org/call-for-contributions/)
https://icpe2020.spec.org/workshops/ (https://icpe2020.spec.org/workshops/)
The International Conference on Performance Engineering (ICPE) is the leading international forum for presenting and discussing novel ideas, innovations, trends, and experiences in the field of performance engineering. Today’s systems are complex, rely increasingly on dynamic architectures, and raise continuously important challenges related to end-to-end performance management. This applies equally to emerging domains, such as cyber-physical systems, and the Internet of Things, big data and machine learning environments, cloud/edge/fog infrastructures, and social networks, and also to traditional domains, such as web-based, data centers, mobile and wireless systems, and real-time systems.
ICPE brings together researchers and practitioners to report state-of-the-art and in-progress research on performance engineering of software and systems, including but not limited to performance modeling, analysis, measurement, benchmark design, and run-time performance management. The focus is both on classical metrics such as response time, throughput, resource utilization, and (energy) efficiency, and on the relationship of such metrics to other system properties including but not limited to scalability, elasticity, availability, reliability, cost, sustainability, security, and privacy.
This year’s main theme is “Performance Engineering under Uncertainty”.
Modern systems are subject to multiple sources of uncertainty due to openness, heterogeneity, versatility, and variability. The complexity of managing performance-related concerns under uncertainty is starting to overwhelm even the capabilities of large engineering teams. We are looking for contributions that use techniques to enhance the performance modeling, estimation, and optimization of complex systems while considering their intrinsic uncertainties. At the same time, we are looking for all the contributions that improve the state-of-the-art while analyzing the performance uncertainty of software systems.
TOPICS
Topics of interest include, but are not limited to:
Performance modeling of software
* Languages and ontologies
* Methods and tools
* Relationship/integration/tradeoffs with other QoS attributes
* Analytical, simulation, and statistical modeling methodologies
* Machine learning and neural networks
* Model validation and calibration techniques
* Automatic model extraction
* Performance modeling and analysis tools
* Traceability of software and performance artifacts
* Control of software performance evolution
Performance and software development processes/paradigms
* Software performance patterns and anti-patterns
* Software/performance tool interoperability (models and data interchange
formats)
* Performance-oriented design, implementation and configuration management
* Software Performance Engineering and Model-Driven Development
* Gathering, interpreting and exploiting software performance annotations
and data
* System sizing and capacity planning techniques
* (Model-driven) Performance requirements engineering
* Relationship between performance and architecture
* Collaboration of development and operation (DevOps) for performance
* Performance and agile methods
* Performance in Service-Oriented Architectures (SOA) and serverless
computing
* Performance of microservice architectures and containers
* DevOps and performance
Performance measurement, monitoring, and analysis
* Performance measurement and monitoring techniques
* Analysis of measured application performance data
* Application tracing and profiling
* Workload characterization and modeling techniques
* Experiment design
* Tools for performance testing, measurement, profiling, and tuning
Benchmarking
* Performance metrics and benchmark suites
* Benchmarking methodologies
* Development of parameterizable, flexible benchmarks
* Benchmark workloads and scenarios
* Use of benchmarks in industry and academia
Run-time performance management and adaptation
* Machine learning and runtime performance decisions
* Context modeling and analysis
* Runtime model estimation
* Use of models at run-time
* Online performance prediction
* Autonomic resource management
* Utility-based optimization
* Capacity management
Power and performance, energy efficiency
* Power consumption models and management techniques
* Tradeoffs between performance and energy efficiency
* Performance-driven resource and power management
Performance modeling and evaluation in different environments and application domains, including but not limited to:
* Cyber-physical systems
* Internet of Things and Industrial Internet (Industry 4.0)
* Communication networks, and embedded, mobile, and wireless systems
* Web-based systems, e-business, Web services
* Big data systems and data analytics
* Machine Learning and Deep-learning systems
* Social networks
* Peer-to-peer systems, including emerging areas such as Blockchain
* Autonomous/adaptive systems
* Transaction-oriented and database systems
* Parallel and distributed systems
* Multi-core, HPC, and other parallel systems
* Cluster, cloud/edge/fog, and grid computing environments
* Control and event-based systems
* Real-time and multimedia systems
Program Committee
Research Papers
* Aldeida Aleti, Monash University, Australia
* Sven Apel, University of Saarland, Germany
* Alberto Avritzer, eSulabSolutions, USA
* Steffen Becker, University of Stuttgart, Germany
* Simona Bernardi, Universidad de Zaragoza, Spain
* Cor-Paul Bezemer, University of Alberta, Canada
* Andre Bondi, Software Performance and Scalability Consulting LLC, USA
* Luca Bortolussi, University of Trieste, Italy
* Ivona Brandic, Vienna University of Technology, Austria
* Francisco Brasileiro, UFCG, Brazil
* Radu Calinescu, University of York, England
* Mihai Capota, Intel, USA
* Valeria Cardellini, University of Rome Tor Vergata, Italy
* Lucy Cherkasova, ARM Research, USA
* Vittorio Cortellessa, University of L'Aquila, Italy
* Vittoria De Nitto Persone', University of Rome Tor Vergata, Italy
* Antinisca Di Marco, University of L'Aquila, Italy
* Antonio Filieri, Imperial College London, England
* Wilhelm Hasselbring, Kiel University, Germany
* Nikolas Roman Herbst, University of Würzburg, Germany
* Sascha Hunold, Vienna University of Technology, Austria
* Pooyan Jamshidi, University of South Carolina, USA
* Zhen Ming Jack Jiang, York University, Canada
* Evangelia Kalyvianaki, University of Cambridge, England
* Samuel Kounev, University of Würzburg, Germany
* Heiko Koziolek, ABB Corporate Research, Germany
* Diwakar Krishnamurthy, University of Calgary, Canada
* Philipp Leitner, Chalmers | University of Gothenburg, Sweden
* Marin Litoiu, York University, Canada
* Catalina M. Lladó, Universitat Illes Balears, Spain
* Martina Maggio, Lund University, Sweden
* Daniel Menasce, George Mason University, USA
* José Merseguer, Universidad de Zaragoza, Spain
* Raffaela Mirandola, Politecnico di Milano, Italy
* John Murphy, School of Computer Science and Informatics, University
College Dublin, Ireland
* Dušan Okanovic, University of Stuttgart, Germany
* Diego Perez-Palacin, Linnaeus University, Sweden
* Dorina Petriu, Carleton University, Canada
* Paolo Romano, INESC-ID/IST, Portugal
* Martin Schulz, Technical University of Munich, Germany
* Weiyi Shang, Concordia University, Canada
* Evgenia Smirni, College of William and Mary, USA
* Mirco Tribastone, IMT Lucca, Italy
* Animesh Trivedi, Vrije Universiteit, Netherlands
* Petr Tuma, Charles University, Czech Republic
* Alexandru Uta, Vrije Universiteit Amsterdam, Netherlands
* André van Hoorn, University of Stuttgart, Germany
* Ana Lucia Varbanescu, University of Amsterdam, Netherlands
* Enrico Vicario, University of Florence, Information Engineering
Department, Italy
* Murray Woodside, Carleton University, Canada
Artifact Evaluation Committee
* André Bauer, University of Würzburg, Germany
* Jinfu Chen, Concordia University, Canada
* Daniele Di Pompeo, Università dell' Aquila, Italy
* Holger Eichelberger, University of Hildesheim, Germany
* Matthew Forshaw, Newcastle University, UK
* Abel Gómez Llana, Universitat Oberta de Catalunya, Spain
* Elena Gómez-Martínez, Universidad Autónoma de Madrid, Spain
* Vojtech Horky, Charles University, Czech Republic
* Emilio Incerto, Gran Sasso Science Institute, Italy
* Shady Issa, University of Lisbon, Portugal
* Colin Paterson, University of York, UK
* Alessandro Pellegrini, Sapienza University of Rome, Italy
* Diego Perez Palacin, Linnaeus University, Sweden
* Jose Ignacio Requeno, INP - Ensimag, Grenoble, France
* Alexandru Uta, Vrije Universiteit Amsterdam, Netherlands
Industry Papers
* Alexander Podelko (Oracle)
* Meikel Poess (Oracle)
* Christian Vögele (Novatec)
* Paul Brebner (Instaclustr)
* Johannes Kroß (fortiss)
* Wolfgang Gottesheim (Dynatrace)
* Simon Spinner (IBM)
* Klaus-Dieter Lange (HPE)
* Martin Klier (Performing Databases)
* Thomas Falkenberg (Payback)
|
